Department of Chemistry, Dibrugarh University, Dibrugarh, 786004, Assam, India.
Department of Chemistry, Dibrugarh University, Dibrugarh, 786004, Assam, India.
Prog Biophys Mol Biol. 2022 Nov;175:31-48. doi: 10.1016/j.pbiomolbio.2022.08.007. Epub 2022 Aug 28.
Aberrant protein folding known as protein misfolding is counted as one of the striking factors of neurodegenerative diseases. The extensive range of pathologies caused by protein misfolding, aggregation and subsequent accumulation are mainly classified into either gain of function diseases or loss of function diseases. In order to seek for novel strategies for treatment and diagnosis of neurodegenerative diseases, insights into the mechanism of misfolding and aggregation is essential. A comprehensive knowledge on the factors influencing misfolding and aggregation is required as well. An extensive experimental study on protein aggregation is somewhat challenging due to the insoluble and noncrystalline nature of amyloid fibrils. Thus there has been a growing use of computational approaches including Monte Carlo simulation, docking simulation, molecular dynamics simulation in the study of protein misfolding and aggregation. The review presents a discussion on molecular dynamics simulation alone as to how it has emerged as a promising tool in the understanding of protein misfolding and aggregation in general, detailing upon three different aspects considering four misfold prone proteins in particular. It is noticeable that all four proteins considered in this review i.e prion, superoxide dismutase1, huntingtin and amyloid β are linked to chronic neurodegenerative diseases with debilitating effects. Initially the review elaborates on the factors influencing the misfolding and aggregation. Next, it addresses our current understanding of the amyloid structures and the associated aggregation mechanisms, finally, summarizing the contribution of this computational tool in the search for therapeutic strategies against the respective protein-deposition diseases.
异常的蛋白质折叠，即蛋白质错误折叠，被认为是神经退行性疾病的一个显著因素。由蛋白质错误折叠、聚集和随后的积累引起的广泛的病理学主要分为功能获得性疾病或功能丧失性疾病。为了寻求治疗和诊断神经退行性疾病的新策略，了解错误折叠和聚集的机制是至关重要的。需要全面了解影响错误折叠和聚集的因素。由于淀粉样纤维的不溶性和非晶态性质，对蛋白质聚集的广泛实验研究具有一定的挑战性。因此，在研究蛋白质错误折叠和聚集时，越来越多地使用计算方法，包括蒙特卡罗模拟、对接模拟、分子动力学模拟。本综述主要讨论了分子动力学模拟，以及它如何作为一种有前途的工具，一般来说，在理解蛋白质错误折叠和聚集方面，详细说明了三个不同的方面，特别是考虑了四个易出错的蛋白质。值得注意的是，本综述中考虑的所有四种蛋白质，即朊病毒、超氧化物歧化酶 1、亨廷顿蛋白和淀粉样β，都与慢性神经退行性疾病有关，这些疾病会导致身体衰弱。首先，本综述详细阐述了影响错误折叠和聚集的因素。接下来，它讨论了我们目前对淀粉样结构和相关聚集机制的理解，最后，总结了这种计算工具在寻找针对相应蛋白沉积疾病的治疗策略方面的贡献。
